Blind Source Separation (BSS) is a technique applied to separate a plurality of original signal sources from an output mixed signal under a condition that the original signal sources collected by a plurality of signal input devices (such as microphones) are unknown. However, the BSS technique cannot further identify the separated signal sources. For example, if one of the signal sources is speech, and the other of the signal sources is noise, the BSS technique can only separate these two signals from the output mixed signal, and cannot further identify which one is speech and which one is noise.
There are conventional techniques for further identifying which separated signal source is speech and which separated signal source is noise. For instance, in Japanese Patent Publication Number JP2002023776, “Kurtosis” of a signal is utilized to identify if the signal is speech or noise. The technique of the publication is based on the facts that a noise signal has a normal distribution whereas a speech signal has a sub-Gaussian distribution. When the distribution of a signal becomes more normal, this represents that there is less Kurtosis. Hence, it is mathematically possible to use Kurtosis for identifying a signal.
However, in the real world, sounds not only have speech and random noise mixed therein, but also include other non-speech sounds, such as music. Since these non-speech sounds, such as music, do not have a normal distribution, they cannot be distinguished from speech sounds using Kurtosis features of signals.